BinConvolution doen't seem to match paper

`(*)` means conv operation, `o` is element-wise product

```
# https://github.com/allenai/XNOR-Net/blob/master/models/alexnetxnor.lua#L16
local function BinConvolution(nInputPlane, nOutputPlane, kW, kH, dW, dH, padW, padH)
local C= nn.Sequential()
C:add(nn.SpatialBatchNormalization(nInputPlane,1e-4,false))
C:add(activation())
C:add(cudnn.SpatialConvolution(nInputPlane, nOutputPlane, kW, kH, dW, dH, padW, padH))
return C
end
```
In this implementation, after input `activation()` next step is direct convolution `cudnn.SpatialConvolution()` with parameters. But The paper's algorithm for Input binarization is:

```
I * W ~= (sign(I) (*) sign(W)) o Ka = ((sign(I) (*) sign(W)) o (A (*) k)a
where
A = torch.mean(input.abs(), 1, keepdim=True)
k = an averaging kernel with value 1/(w*h)
```
so
`A(*)k` is to averaging each input element with its neighboring elements. This is ***missing*** in the current implementation, where only `(sign(I) (*) sign(W)) o a` is calculated.
```
C:add(activation())
C:add(cudnn.SpatialConvolution(nInputPlane, nOutputPlane, kW, kH, dW, dH, padW, padH))
```

To capture the convolution with `A` and `k` from the paper, I would expect **pseudo** code like this in `function BinConvolution()` **in python**
```
x = BinActiveZ(x)
# <=== === === === === === === === === === START
A = mean #shape N, 1, W, H
sign_I = x #shape N, Cin, W, H
kH = self.conv.weight.shape[2] #kernel height
kW = self.conv.weight.shape[3] #kernel width
k = torch.ones(1, 1, kH, kW) * (1/(kH*kW)) #setup averaging kernel k
conv_Ak = torch.nn.Conv2d(1, 1, kH, kW, padding=(kH//2, kW//2))
conv_Ak.weight.data = k
K = conv_Ak(A) #shape N, 1, W, H

#now calculate sign_I (*)sign_W o Ka
# since self.conv.weight is already binarized by binarizeConvParams() before batch starts,
# the `a` in `Ka` is included in `self.conv(x)` . The only missing part is `mul(K)`
# Hence:
x = self.conv(x).mul(K)
# <=== === === === === === === === === === END
```

Can you check if my understanding of the discrepancy is correct?